Methodology for automated signal morphology analysis in implantable electrotherapy and diagnostic systems

ABSTRACT

Systems and related methods for analyzing data sensed from a device implanted in a patient, such as a cardiac pacing system. The system detects and evaluates electric signals within the patient that share a common event marker. By using algorithms and graphical presentation of the collected signals having common event markers, deviations in signals over time can be identified and evaluated in consideration of taking further action related to the patient and the implanted device. The system can also be used in conjunction with an advanced patient management system that includes a programmer or repeater capable of gathering information from the implanted device and transmitting the data to a host via a communications network for evaluation at a remote location.

TECHNICAL FIELD

The present method relates generally to implantable medical devices andmore particularly relates to systems and methods for analyzingelectrical signals present in a patient based on event classification ofthe signals.

BACKGROUND

Management of patients with chronic disease poses many challenges.Chronic diseases such as heart failure, asthma, COPD, chronic pain, andepilepsy, are event driven. Some example implantable devices for use inheart disease treatment include pacemakers, implantable cardioverterdefibrillators (ICDs), and heart failure cardiac resynchronizationtherapy (CRT) devices. Often, implanted devices are used to monitor apatient's condition before, during, and after treatments applied by theimplanted device. Implanted devices are often designed to monitorconditions and performance of the device itself.

The implantable devices can be configured to separately monitor or sensethe electro-physiologic data associated with different organs or typesof tissue in a patient (e.g., neurological tissue, cardiac muscle, andskeletal muscle). This data, when appropriately analyzed, can providethe clinician with a valuable diagnostic and/or prognostic tool to moreaccurately assess a status of patient health or the diagnostic state ofthe implantable device.

SUMMARY OF THE INVENTION

The present invention generally relates to systems and methods foranalyzing data sensed from a device implanted in a patient, such as acardiac pacing system. The system detects and evaluates electric signalswithin the patient that share a common event marker. Using algorithms,charts, and graphical presentation of the collected signals havingcommon event markers, deviations in signals over time can be identifiedand evaluated in consideration of taking further action related to thepatient or the implanted device. The system can also be used inconjunction with an advanced patient management system that includes aprogrammer or repeater capable of gathering information from theimplanted device and transmitting the data to a host via acommunications network for evaluation at a remote location.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the invention.Those skilled in the art will readily recognize various modificationsand changes that can be made to the present invention without followingthe example embodiments and applications illustrated and describedherein, and without departing from the true spirit and scope of thepresent invention, which is set forth in the following claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention can be more completely understood in consideration of thefollowing detailed description of various embodiments of the inventionin connection with the accompanying drawings, in which:

FIG. 1 is a schematic diagram illustrating an example method foranalyzing signal morphology in accordance with the present invention.

FIG. 2 is a graph illustrating an example presentation of signalinformation for a group of like event markets.

FIG. 3 is a schematic diagram illustrating an example method forautomated intra-cardiac signal morphology analysis according toprinciples of the present invention.

FIG. 4 is a schematic diagram illustrating another example method forautomated intra-cardiac signal morphology analysis according toprinciple of the present invention, wherein the method includesextracting comparable signal components.

FIG. 5 is a schematic diagram illustrating a further example method forautomated intra-cardiac signal morphology analysis according toprinciple of the present invention, wherein the method includesdetecting signal deviations.

FIG. 6 is a chart illustrating intra-cardiac signal information for agroup of like event markers.

FIG. 7 is a graphic illustration of cardiac signal information for agroup of like event markers that illustrates more and less frequentsignal characteristics via overplotting of the signals.

FIG. 8 illustrates schematically another example system made inaccordance with the present invention.

FIG. 9 illustrates an example advanced patient management system made inaccordance with the present invention.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The systems and methods described herein can be used to analyzeelectrical signals in a patient, such as neurological and cardiovascularwaveforms. The electrical signals can be generated from a signal sourcesuch as, for example, a mechanical sensor, a chemical sensor, or theintrinsic electrical activity generated by a patient's body. The examplesystems and methods disclosed herein provide for the use of an implanteddevice, collection of a signal present in the patient, whether a signalis an intrinsic event sensed by the device or a therapeutic signalgenerated by the device, analysis of the collected signal, and adecision concerning what the signal means. The example systems andmethods can also include presentation of the analyzed collected signalto a physician for visual inspection.

The state and action of the device is referred to as an event marker.Event markers can be identified in part by aspects of the collectedsignal. Identification of event markers can also be identified in partby the known activity of the implantable device.

In one example application, the electrical signals analyzed includecardiovascular data sensed from a cardiac rhythm management (“CRM”)device. This application provides detection and evaluation of changes ina cardiac pacing system using a methodology of combined event marker andintra-cardiac signal analysis. Telemetric pulse generator (“PG”) eventmarkers can be used for identifying the morphologies and sequences ofsignal components. While the present disclosure emphasizes cardiacapplications, general principles disclosed herein can also be used foranalysis and evaluation of neurological waveforms and waveforms relatedto skeletal muscle.

I. Signal Morphology Analysis

Referring to FIG. 1, an example method of analyzing electrical activityin a patient is described. The method includes a step 2 of monitoringelectrical signals within the patient using an implanted device, and astep 4 of identifying event markers that correspond to aspects of theelectrical signals. The method of FIG. 1 also includes a step 6 ofcategorizing into groups or bins the aspects of the electrical signalsrepresenting a type or classification of event markers, and a step 8 ofcomparing the aspects of the electrical signals within at least one ofthe groups to identify changes over time.

The method of FIG. 1 can also include other steps such as, for example,reporting the comparison for analysis by a physician, wherein thechanges over time include occurrences outside of a predetermined rangeof electrical signal values. The method can also include analyzingidentified changes over time to determine a recommended action. The step2 of monitoring the electrical signals can include monitoringintra-cardiac electrograms generated by an implanted cardiac rhythmmanagement device. Aspects of the electrical signal can include waveformdata, wherein the waveform data in conjunction with the event markers,can be used to identify threshold deviations in the waveform data overtime.

The event markers of step 4 can be used to help define boundaries ofperiods of the electrical signal. Those portions of the signal definedby the event markers are separated into separate groups or bins for eachtype of event marker. The periods of the signal can be any desired timethat captures at least a portion of the signal representative of whathas occurred due to the event marker. For example, the period may beabout 100 to about 2000 milliseconds, and more preferably about 200 toabout 1000 milliseconds. In some configurations, the time period is oneheart beat, one muscle contraction, or one nerve signal or stimulus. Thesystem can be designed to capture the period of the signal for eachevent marker and place that period of the signal in the bin designatedfor that given event marker. In some embodiments, the periods arecaptured a certain number of times within a given time frame (e.g., onetime per hour), or are captured after a certain number of occurrences(e.g., one capture for every four occurrences of the period for a giventype of event marker).

When using device in the patient for an extended time period (e.g.,several hours, days, weeks, or years), a number of captures of periodsof the signal for a given event marker are binned and available foranalysis. The system executing steps of the example method describedabove analyzes the binned periods of the signals. Analysis of the binnedperiods can be handled in a variety of ways. In one example, theanalysis is communicated in the form of a graph whereon the periods ofthe signals are plotted on top of each other, such as illustrated inFIG. 2. The graph illustrates trends over time and abnormalities. Theinformation presented on the graph can be evaluated automatically by aelectronic device such as implanted device from which the event markersate determined, a device outside of but located near the patient, or adevice located remote from the patient. The analysis of binned periodscan also be evaluated by such devices when the information is in otherformats than in a graph. Automated evaluation by an electronic devicetypically involves execution of an algorithm (not shown) using aprocessor or other analysis device. Such algorithms can be part of acomputer program.

The graft can also be presented to and viewed by the patient, thepatient's physician, or a third party for further analysis. The graphedinformation can be presented in different colors, with different stylesof lines, or other characteristics that help distinguish between, forexample, subcategories of the binned periods or abnormal values. Inanother embodiment, the analysis information can be presented in a chartformat (e.g., see FIG. 6) or other formats best suited for a givenapplication. Review of the presented information can be used todetermine whether further action (e.g., treatment, testing, repair) isneeded.

Analysis of the binned information can be centered around monitoring ofconsistent performance of the periods of the signal within a bin.Departures outside of the consistent performance (e.g., outside of apredetermined range of values) can indicate a problem or the potentialof a problem occurring, for example, with the patient's health (e.g.,improper response to treatments) or the implanted device. Analysis ofthe binned periods can also provide information about how ofteninconsistent performance occurs.

The binned periods of signals can be averaged over different timeperiods and that information evaluated. For example, the periods ofsignal captured in a bin during a set time interval (e.g., a day, aweek, or a month) can be averaged for that time interval and thencompared to the average values from previous and future occurrences ofthat time interval.

The bins or groups related to different event markers can be further oradditionally categorized based on other physical indicia such as, forexample, heart rate and blood pressure.

While the above description uses an implanted device as an importantpart of carrying out functions of the system, other devices can providesimilar results. For example, devices secured to an outer surface of thepatient (e.g., a surface echocardiogram) can be used to generatesignals, sense electrical signal, and provide a basis for identifyingevent markers.

II. Example Systems and Methods for Automated Intra-Cardiac SignalMorphology Analysis

Another example system 10 is shown and described with reference to FIGS.3-7. The system 10 is directed to a system for automated intra-cardiacsignal morphology analysis.

When a elemetric pulse generator (PG) or other implanted device deliverstherapy (e.g., in the form of electronic stimuli or shocks), the PG canbe adapted to record or mark the delivered therapy event in a memorycomponent of the device. The PG can further be adapted to record thesensing events of a lead of the PG that is couple to the patient (e.g.,to the patient's heart). This allows a clinician to interrogate thedevice's memory to obtain a historical view of the patient's healthstatus and any therapy delivered by the PG.

Cardiac rhythm management (“CRM”) devices can be used with an externaldevice such as a programmer or controller that communicates with theCRM. Programmers are typically adapted to electronically communicatewith a CRM by sending data to and receiving data from the device.Physicians or other medical personnel use such controllers to monitorand control the functions of the implanted device typically duringin-person visits by the patient to a clinic.

An external device can also be configured to interrogate a CRM orPG/Lead system. In such a configuration, the device is often referred toas a communicator or repeater. The monitoring or interrogation mode ofan external programmer or repeater is primarily used as a diagnostictool to evaluate the implanted device and patient between office visits.However, CRM interrogation typically does not involve any temporalcorrelation and/or analysis of an operational event of the CRM with aphysiologic response or event sensed by the CRM. With the use of deviceand/or sensed event markers as points of reference, the clinician has atemporal record of data that can be correlated and analyzed to detectany changes or anomalies with the device or with the expectedtherapeutic results. A temporal correlation can not only provide theclinician with valuable information on the operation of the CRM and itsleads, but also provide the clinician with equally valuable informationon the clinical status of the patient.

When such an external device is connected to a remote server ordatabase, the information can be made available to any clinician. Thus,a clinician or specialist in a remote location can monitor and evaluatethe PG/Lead system and the patient and potentially reduce the frequencyof in-office assessments or, conversely, quickly identify the need for apersonal assessment based on analysis of the information transmitted tothe server or database.

The example systems and method described herein are configured toevaluate an implanted PG/Lead system based on a PG event marker and/orintra-cardiac electrograms (“EGMs”) that allow a clinician todistinguish between changing signal morphologies in an implanted systemthat represent, for example, a device malfunction, an unexpected signalsource, or a change in the clinical state of a patient. The examplesystems and methods allow a clinician in a remote setting to determinethe frequency of, or urgent need for, an in-office evaluation based uponthe analysis of marked events. In these ways, the systems and methodsdisclosed herein can assist in lowering the cost of medical care byproviding remote oversight of the operation and therapy provided by anPG/Lead system and increasing awareness of physiologic systeminfluences.

One example system uses a plurality of intra-cardiac event markersincluding, but not limited to, Atrial Sense (“AS”), Atrial Pace (“AP”),Left Ventricular Sense (“LVS”), Left Ventricular Pace (“LVP”), RightVentricular Sense (“RVS”), and Right Ventricular Pace (“RVP”) markers.These lead specific PG markers can then be analyzed to identify commonor uncommon events associated with a specific event marker. By way ofnon-limiting example only, the AS marker can be graphically displayedover a period of time representing a series of AS events that allow theclinician to quickly and visually observe any deviations.

The EGMs can be organized in bins for different event markers. The EGMscan be represented by predefined time intervals such as, for example, asingle beat of the heart rate at the time the EGM was recorded. Binningpermits separation of paced events at different rate thresholds. Forexample, a waveform of paced events can be analyzed and compared in viewof a maximum tracking rate (“MTR”) versus paced events at a lower ratelimit (“LRL”). MTR and LRL can be considered the extremes of normalbehavior for a dual chamber pace maker. In one embodiment, the systemprovides separation of paces and senses that occurred during alteredpacing support states, such as post-shock pacing, atrial tachycardiaresponse mode switching, etc. In this way the system provides drawing ofEGMs within a bin over each other, so that the clinician can observe thefrequency at which different waveforms are detected. More frequentwaveforms are drawn in a “hotter” color (e.g., red), which would givethe clinician a sense of not only what waveforms are occurring, but alsohow often those waveforms are occurring for the patient.

In a further example system, the same signal and marker information canbe correlated against expected values to identify deviations in signalmorphology. For example, an expected AS event can be translated into analgorithmic expression that can include an acceptable range ofdeviation. If an AS event occurs outside the acceptable range, thatevent can be flagged or identified for further evaluation and analysisby a clinician. Patients without events demonstrating intra-cardiacsignals significantly different in morphology can be eligible to areduced frequency of in-office evaluation. Conversely, events presentingthemselves outside the expected range for signal morphology variationscan prompt an increased frequency of in-office evaluation.

Example systems and methods disclosed herein operate from a premise thattypical signal morphologies of events in an EGM are relatively stable.Any change in morphology is associated with a specific root cause orcondition. Conditions that can have an influence on signal morphologies,amongst others are:

Oversensing

Undersensing

Loss of Capture

Dislocation

Lead Fracture

Body Posture

Time of day

Patient Salt Intake

Medication

Exercise

A device associated with the afore-mentioned example systems and methodscan include an external device such as, for example, a programmer orrepeater that is compatible with a PG/Lead system. Such an externaldevice is typically used to monitor and control the operation of thePG/Lead system. In addition, the programmer or repeater can beconfigured to interrogate and record the event markers recognized by thePG/Lead system. The programmer or repeater is capable of storing anamount of data that typically far exceeds the capabilities of thePG/Lead system. This stored data can be made accessible to a clinicianwho analyzes and evaluates historical patient data, including comparingpatient data collected from different patients. The clinician analysiscan be done remotely as well as during in-clinic patient visits.

The programmer or repeater can be a component of an advanced patientmanagement (“APM”) system or network adapted to store and analyzediverse patient data and information (see below for further details ofan APM application). In an APM, population data can also be accessibleto the clinician so that the clinician can analyze a specific patient'sdata within the context of a population of patients. This type ofanalysis can reveal or disclose the impact of environmental factors on astatus of patient health.

Some example systems and methods use algorithmic and graphicalpresentations of sensed events and deviations therefrom. Such systemsand methods can employ the use of a PG/Lead system with memorycapabilities to allow capturing of sensed events over an extended timeperiod. Some systems can also include a programmer or repeater capableof interrogating the data stored in the PG/Lead system and transmittingthat data to a computer network that is part of, for example, an APMsystem.

The use of event markers for filtering intra-cardiac signals into mainevent morphologies can provide several advantages such as filter outsignal sequences that are market as noise by the device, using thePG-based event marker classification by the device to identify mainsignal components (AS, AP, RVS, RVP, LVS, LVP), isolate signalcomponents with a sufficient amount of signal time prior to and afterthe event marker, and application of a mathematical signal processingmethod to those components within one group to identify eithersignificantly common or different signal morphologies. In a securelyimplanted and programmed PG/Lead system, one or more of the six majorsignal components will have a repetitive morphology, includingconsistent morphology of outliers indicating under- or over-sensing aswell as non-capture situations when the PG/Lead system fails to sense anevent marker or capture an event marker.

The use of event markers for filtering intra-cardiac signals into mainevent sequences can provide several advantages such as filtering outsignal sequences that are market as noise by the device, using the PGbased event marker classification by the device to identify main signalcomponents (AS, AP, RVS, RVP, LVS, LVP), and isolating individualsequences of signals with a sufficient amount of signal time prior andafter to the event sequence. A search within the event markers canresult in identifying one or more of the following four main eventsequences:

Atrial Sense Event followed by Ventricular Sense Event AS-VS

Atrial Pace Event followed by Ventricular Sense Event AP-VS

Atrial Sense Event followed by Ventricular Pace Event AS-VP

Atrial Pace Event followed by Ventricular Pace Event AP-VP

Another advantage includes the ability to apply a mathematical signalprocessing method to those signal sequences within one group toautomatically identify either significantly common or different signalmorphologies. Another advantage is that a clinician can observe, detectand review changes. A still further advantage is that additionalsubdivisions of sensing and pacing sequences can be employed dependingon the needs of the patient.

In an implanted and programmed PG/Lead system, major signal/markersequences usually have a repetitive morphology. Some example repetitivemorphologies include consistent morphology of outliers indicating under-or over-sensing as well as non-capture situations when the PG/Leadsystem fails to sense an event marker or capture an event marker.

A patient's diet can also have an effect on signal morphologies. Forexample, salt intake can affect myocardial electrophysiology. Thus, aPG/Lead system configured to sense serum sodium can correlate a changein serum sodium to a sensed physiologic change or a change intherapeutic modality. In this way, the methodology can also promoteheightened awareness of external influences on physiologic parameters.

Example systems and methods disclosed herein also allow for an automatedevaluation/identification of such influencing factors on an implantedPG/Lead system. Extracted information can be used to optimize PGprogramming or other medical therapy such as, for example,pharmaceutical therapy.

The system 10 shown in FIG. 3 includes a PG 12 having a lead 14 that iscoupled to a patient's heart 16. The PG includes a pacing module 18, asensing module 20, and a marker module 22. Signals 15 transmitted viathe lead 14 generally comprise intra-cardiac electrogram signals fromthe heart 16. The signals 15 reflect the electro-physiologic state ofthe patient's heart 16. These signals generally indicate, among otherthings, a measure of the patient's cardiac cycle, the contractility ofthe patient's heart chambers, and the potential presence of pulmonaryobstructions such as pulmonary emboli. Signals 15, if deviant fromexpected norms, will typically trigger the PG 12 to emit pacing stimulior shocks to the heart 16 to restore normal cardiac function.

The lead 14 is used to deliver pacing energy to cardiac tissue as wellas sense cardiac electrical activity. Sensing using the PG 12 producestwo data outputs of interest—an amplified version of measured cardiacactivity 24 and sensed markers 26. Pacing produces one data output ofinterest—pacing markers 28. The marker module outputs the sensed markers26 and pacing markers 28 as markers 30 from the PG 12. The markers 30and signal output 24 are subsequently extracted, compiled and processedto create indications such as, for example, lead issues, sensing issuesand physiologic changes.

FIG. 4 illustrates aspects of another example system 40 that includesfirst stage signal processing for detection of short and long-termevents. System 40 can be a related to system 10 shown in FIG. 3 in thatthe signal 24A and markers 30A used as input to the system 40 can be theoutputs 24, 30 from system 10.

The functional blocks Delay 1 (41), Delay 2 (42), Delay 3 (43) andThreshold 44 can represent a single channel or multiple channelprocesses if there are parametric variations in operational parametersneeded for selected outputs. The essential characteristic isdifferentiation of a single cycle comparison versus differences overmultiple cycles. The threshold function is not limited to fixedthreshold operations. Thresholding can incorporate fixed or adaptiveoperation and it can also incorporate hysteresis or other higher orderlinear or non-linear functions to sustain indication of onset or releaseof a particular event. The output of thresholds 44 are short- andlong-term comparisons 51, 52 that can be used to determine particularevents.

Delay functions 41, 42, 43 have dynamic lengths adjusted to match heartrate. The length of delay is controlled by periodic marker eventindications with the intent of synchronizing features in the currentcardiac cycle with the same features in a previous cardiac cycle. Delay1 (41) is used to time align the beginning of a cardiac cycle with anintra-cardiac cycle feature. The Delay 1 (41) period can be seconds,minutes, days or any period that allows contextually equivalentanalysis. The system 40 also includes averaging modules 45, 46, 47 andsumming modules 48, 49 to assist in detecting the short- and long-termevents.

FIG. 5 illustrates aspects of another example system 50 that includesanalysis of short 51A and long-term 52A comparisons that can lead toconclusions about system faults. System faults can include, but are notlimited to, lead dislodgements, lead fractures, generatormisprogramming, reduced output, etc. A feature of this approach is thatit uses various time constants to differentiate physiological events.The Persistent Signal Detectors 54, 55 (“PSD”) can, for example, beconstructed from an integrator followed by a threshold. PSD output willindicate after an input persists for some T (interval) average amount oftime. Occasional Signal Present detectors 56, 57 are activated when asingle or small number of events occur. A Frequent Changes of Statedetector 58 indicates when a specified number of input state changesoccur within a given time period of time.

Switches 60, 61 of system 50 indicate a gating function over which thecomparison is meaningful. One example is gating the comparison functionduring a cardiac cycle to observe differences resulting from loss ofcapture 62, but sensing of electrical activity during times other thanwhen the core cardiac cycle can indicate over-sensing 64. The system 50includes other outputs indicating, for example, undersensing 66, leaddislodgement or fracture 68, and physical conditions 70 correspondingto, for example, salt intake, medications, exercise, and body posture.

FIG. 6 illustrates a chart 80 that can be used to check system integrityfor systems such as, for example, systems 10, 40, 50 described above.Based on transmitted EGM signals and markers of PG events, a PG/Leadsystem integrity check can be performed through comparing morphologicsignal criteria (not shown) and evaluating whether an individualwaveform is within an expected range of variation or outside theexpected range. This comparison and evaluation can provide an indicationof whether the PG/Lead is functioning properly. The examination of EGMmarker data can be done by visualizing whether signal waveforms (shownin FIG. 7) match corresponding PG markers in a graph format. The chart80 can include an example “common” signal in the “common events” column82 that can be compared visually or mathematically using algorithms tothose events that are or can be considered “uncommon” (included in the“uncommon events” column 84).

FIG. 6 also provides an overview of the results of the morphologicevaluation in the form of an event-by-event or event sequence-by-eventsequence visualization of event markers 86. As further shown in FIG. 6,binning or grouping waveform data according to a heart chamber to detectdeviations in waveform morphology or to check system integrity canassist a clinician in evaluating a patient's unstable event morphology.This graphical aspect of the method provides an efficient process foridentification of EGMs to the clinician during an in-clinic or remotedevice/system evaluation. While the chart 80 shows only two columns forseparating signals into Common and Uncommon events, the signal can bedivided into other categories gradated between extremes of common anduncommon events.

FIG. 7 is a graphic illustration of cardiac signal information for agroup of like event markers that illustrates more and less frequentsignal characteristics via overplotting of the signals. Presentation ofthe signal information in this graphic format can be advantageous forpurposes of obtaining a quick overview of trends and performance basedon like event markers.

The example systems and methods identify changes in intra-cardiac signalmorphology using algorithmic analysis. By building on the PG eventclassification, significantly different signal morphologies can bequickly and efficiently identified. The algorithms (not shown) andmethods useful in accordance with the present disclosure can utilize thePG markers to identify typical components of the intra-cardiac signaland evaluate the PG decision process. Further, information of thephysiologic process can be revealed. PG marker and system integrity datacan also be analyzed using a computer network database system such asthe Advanced Patient Management (“APM”) system described below.

II. Advanced Patient Management System

Referring now to FIGS. 8 and 9, an example system 100 for collecting andanalyzing patient data is illustrated. System 100 includes a device 102,an interrogatory/transceiver unit (ITU) or repeater 108, acommunications system 110, and a host 112. The system 100 can be usedto, for example, communicate information to a physician or other thirdparty for evaluation of binned signals and related event markerinformation. Further details related to advanced patient managementsystems are disclosed in U.S. Published Patent Application No.2004/0127958, entitled ADVANCED PATIENT MANAGEMENT SYSTEM INCLUDINGINTERROGATOR/TRANSCEIVER UNIT, which published application isincorporated herein by reference in its entirety.

FIG. 9 illustrates an example APM system 600 that generally includes thefollowing components: devices 602, 604, and 606, aninterrogator/transceiver unit 608, a communication system 610, and ahost 612. Each component of the APM system 600 can communicate using thecommunication system 610. The host 612 includes a database module 614,an analysis module 616, and a delivery module 618. Host 612 preferablyincludes enough processing power to analyze and process large amounts ofdata collected from each patient, as well as to process statistics andperform analysis for large populations. For example, the host 612 caninclude a mainframe computer or multi-processor workstation. The host612 can also include one or more personal computer systems containingsufficient computing power and memory. The host 612 can include storagemedium (e.g., hard disks, optical data storage devices, etc.) sufficientto store the massive amount of high-resolution data that is collectedfrom the patients and analyzed.

The database module 614 includes a patient database 630, a populationdatabase 632, a medical database 634, and a general database 636. Theanalysis module 616 includes a patient analysis module 640, deviceanalysis module 642, population analysis module 644, and learning module646.

In one embodiment, the data collected and integrated by the advancedpatient system 600, as well as any analysis performed by the system 600generally, is delivered by delivery module 618 to a caregiver's hospitalcomputer system for access by the caregiver. A standard or custominterface facilitates communication between the APM system 600 and alegacy hospital system used by the caregiver so that the caregiver canaccess all relevant information using a system familiar to thecaregiver.

The APM system 600 can also be configured so that various components ofthe system (e.g., ITU 608, communication system 610, and/or host 612)provide reporting to various individuals (e.g., patient and/orcaregiver). For example, different levels of reporting can be providedby (1) the ITU 608 and (2) the host 612. The ITU 608 can be configuredto conduct rudimentary analysis of data gathered from devices 602, 604,606, and provide reporting should an acute situation be identified.

In addition to forms of reporting including visual and/or audibleinformation, the APM system 600 can also communicate with andreconfigure one or more of the devices 602, 604, 606. For example, ifdevice 602 is part of a cardiac rhythm management system, the host 612can communicate with the device 602 and reconfigure the therapy providedby the cardiac rhythm management system based on the data collected fromone or more of the devices 602, 604, 606. In another embodiment, thedelivery module 618 can provide to the ITU 608 recorded data, an idealrange for the data, a conclusion based on the recorded data, and arecommended course of action. This information can be displayed on theITU 608 for the patient to review or made available on the peripheraldevice 609 for the patient and/or clinician to review.

III. Conclusion

One or more headings have been provided above to assist in describingthe various embodiments disclosed herein. The use of headings, and theresulting division of the description by the headings, should not beconstrued as limiting in any way. The subject matter described under oneheading can be combined with subject matter described under one or moreof the other headings without limitation and as desired.

The systems and methods of the present disclosure can be implementedusing a system as shown in the various Figures disclosed hereinincluding various devices and/or programmers, including implantable orexternal devices. Accordingly, the methods of the present disclosure canbe implemented: (1) as a sequence of computer implemented steps runningon the system; and (2) as interconnected modules within the system. Theimplementation is a matter of choice dependent on the performancerequirements of the system implementing the method of the presentdisclosure and the components selected by or utilized by the users ofthe method. Accordingly, the logical operations making up theembodiments of the method of the present disclosure described herein canbe referred to variously as operations, steps, or modules. It will berecognized by one of ordinary skill in the art that the operations,steps, and modules can be implemented in software, in firmware, inspecial purpose digital logic, analog circuits, and any combinationthereof without deviating from the spirit and scope of the presentinvention as recited within the claims attached hereto.

The above specification, examples and data provide a completedescription of the manufacture and use of the composition of theinvention. Since many embodiments of the invention can be made withoutdeparting from the spirit and scope of the invention, the inventionresides in the claims hereinafter appended.

1. A method of monitoring electrical activity in a patient, the methodcomprising: monitoring electrical signals within the patient using animplanted device; identifying sense and pace event markers correspondingto sensed and paced events identified by the implanted device;categorizing into groups time periods of the electrical signalsrepresenting a type of event marker; and comparing the time periods ofthe electrical signals within at least one of the groups to identifychanges over time.
 2. The method of claim 1, further comprisingreporting the comparison for analysis by a physician.
 3. The method ofclaim 2, wherein reporting the comparison includes reporting the changesover time including occurrences of values of the electrical signal beingoutside of a predetermined range.
 4. The method of claim 1, whereincomparing the time periods includes plotting a plurality of the timeperiods on a graph.
 5. The method of claim 4, comprising automaticallyevaluating information presented on the graph using the implanteddevice.
 6. The method of claim 4, comprising automatically evaluatinginformation presented on the graph using a device located near thepatient.
 7. The method of claim 4, comprising automatically evaluatinginformation presented on the graph using a device remote from thepatient.
 8. The method of claim 1, further comprising analyzing theidentified changes over time to determine a recommended action.
 9. Themethod of claim 1, wherein monitoring the electrical signals includesmonitoring intra-cardiac electrograms generated by an implanted cardiacrhythm management device.
 10. The method of claim 9, wherein the timeperiods of the electrical signal include waveform data, and comprisingidentifying threshold deviations in the waveform data over time.
 11. Themethod of claim 1, wherein the electrical signals originate from asignal source selected from the group consisting of a mechanical sensor,a chemical sensor, intrinsic electrical activity generated by thepatient's body.
 12. The method of claim 1, comprising averaging the timeperiods grouped for a time interval over that time interval to producean averaged time period, and comparing the averaged time period toaverage time periods from previous and future occurrences of that timeinterval.
 13. The method of claim 1, wherein comparing the time periodsincludes presenting analysis information in a chart format.
 14. Themethod of claim 1, comprising monitoring consistent performance of thetime periods within a group of the groups.
 15. The method of claim 14,comprising providing information about how often inconsistentperformance of the time periods within the group occurs.
 16. The methodof claim 1, comprising further categorizing the groups of the timeperiods based on heart rate.
 17. The method of claim 1, comprisingfurther categorizing the groups of the time periods based on bloodpressure.
 18. The method of claim 1, wherein the event markers includeat least one of an atrial sense marker, a left ventricular sense marker,a right ventricular sense marker, an atrial pace marker, a leftventricular pace marker, and a right ventricular pace marker.